Systems and methods for predicting security threat attacks

ABSTRACT

A computer-implemented method for predicting security threat attacks may include (1) identifying candidate security threat targets with latent attributes that describe features of the candidate security threat targets, (2) identifying historical attack data that describes which of the candidate security threat targets experienced an actual security threat attack, (3) determining a similarity relationship between latent attributes of at least one specific candidate security threat target and latent attributes of the candidate security threat targets that experienced an actual security threat attack according to the historical attack data, (4) predicting, based on the determined similarity relationship, that the specific candidate security threat target will experience a future security threat attack, and (5) performing at least one remedial action to protect the specific candidate security threat target in response to predicting the future security threat attack. Various other methods, systems, and computer-readable media are also disclosed.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.62/264,201, filed 7 Dec. 2015, the disclosure of which is incorporated,in its entirety, by this reference.

BACKGROUND

Individuals and organizations typically seek to protect their computingresources from sophisticated security threats. Nevertheless, mostcomputing security products focus on addressing attacks after theattacks have occurred. For example, an antivirus program product mayblock a computer virus after detecting that the computer virus is activeon a client computing system. Similarly, a firewall program product mayblock malicious network traffic upon detecting the traffic attempting toreach a client computing system.

Although these computing security products provide a level of protectionagainst corresponding security threats, the products may fail tooptimally protect an enterprise's computing resources. For example,these computing security products may fail to predict or anticipateattacks on the enterprise's computing resources. Consequently, thecomputing security products may fail to take preventive measures thatwould better protect the enterprise from future or predicted attacks.Accordingly, the instant disclosure identifies and addresses a need foradditional and improved systems and methods for predicting securitythreat attacks.

SUMMARY

As will be described in greater detail below, the instant disclosuregenerally relates to systems and methods for predicting security threatattacks by, for example, mathematically analyzing a matrix havingenterprise organizations listed along the rows and types of malwareattacks listed along the columns, as discussed further below. In oneexample, a computer-implemented method for predicting security threatattacks may include (1) identifying candidate security threat targetswith latent attributes that describe features of the candidate securitythreat targets, (2) identifying historical attack data that describeswhich of the candidate security threat targets experienced an actualsecurity threat attack, (3) determining, by a software securityprediction program, a similarity relationship between latent attributesof at least one specific candidate security threat target and latentattributes of the candidate security threat targets that experienced anactual security threat attack according to the historical attack data,(4) predicting, by the software security prediction program based on thedetermined similarity relationship, that the specific candidate securitythreat target will experience a future security threat attack, and (5)performing, by the software security prediction program, at least oneremedial action to protect the specific candidate security threat targetin response to predicting the future security threat attack.

In one embodiment, the candidate security threat targets includeenterprise organizations. In further embodiments, the enterpriseorganizations include customers of a vendor of the software securityprediction program.

In one embodiment, determining the similarity relationship may include(1) identifying an additional candidate security threat target thatexperienced a pair of actual security threat attacks and (2) determiningthat the specific candidate security threat target experienced one ofthe pair of actual security threat attacks. In these embodiments,predicting that the specific candidate security threat target willexperience the future security threat attack may include predicting thatthe specific candidate security threat target will experience the otherof the pair of actual security threat attacks. In some examples,determining the similarity relationship may include: (1) identifying anadditional candidate security threat target that stored a cluster ofbenign files and that experienced a same security threat attack as thepredicted future security threat attack and (2) determining that thespecific candidate security threat target also stored the cluster ofbenign files.

In some examples, determining the similarity relationship may includeanalyzing a matrix that identifies (1) enterprise organizationscorresponding to at least one of rows and columns of the matrix and (2)security threat attacks corresponding to the other of the rows andcolumns of the matrix. In further embodiments, the matrix may include asparse matrix. In some examples, determining the similarity relationshipmay include performing a rank factorization of the matrix. In furtherexamples, performing the rank factorization of the matrix may includeexecuting a stochastic gradient descent algorithm.

In one embodiment, determining the similarity relationship may include(1) ranking candidate security threat targets in terms of counts ofexperiencing actual security threat attacks and (2) ranking securitythreat attacks in terms of actually attacking enterprise organizations.In these examples, predicting that the specific candidate securitythreat target will experience the future security threat attack is basedon the rank of the specific candidate security threat target and therank of the predicted future security threat attack.

In one embodiment, a system for implementing the above-described methodmay include (1) an identification module, stored in memory, thatidentifies candidate security threat targets with latent attributes thatdescribe features of the candidate security threat targets and thatidentifies historical attack data that describes which of the candidatesecurity threat targets experienced an actual security threat attack,(2) a determination module, stored in memory, that determines, as partof a software security prediction program, a similarity relationshipbetween latent attributes of at least one specific candidate securitythreat target and latent attributes of the candidate security threattargets that experienced an actual security threat attack according tothe historical attack data, (3) a prediction module, stored in memory,that predicts, as part of the software security prediction program basedon the determined similarity relationship, that the specific candidatesecurity threat target will experience a future security threat attack,(4) a performance module, stored in memory, that performs, as part ofthe software security prediction program, at least one remedial actionto protect the specific candidate security threat target in response topredicting the future security threat attack, and (5) at least onephysical processor configured to execute the identification module, thedetermination module, the prediction module, and the performance module.

In some examples, the above-described method may be encoded ascomputer-readable instructions on a non-transitory computer-readablemedium. For example, a computer-readable medium may include one or morecomputer-executable instructions that, when executed by at least oneprocessor of a computing device, may cause the computing device to (1)identify candidate security threat targets with latent attributes thatdescribe features of the candidate security threat targets, (2) identifyhistorical attack data that describes which of the candidate securitythreat targets experienced an actual security threat attack, (3)determine, by a software security prediction program, a similarityrelationship between latent attributes of at least one specificcandidate security threat target and latent attributes of the candidatesecurity threat targets that experienced an actual security threatattack according to the historical attack data, (4) predict, by thesoftware security prediction program based on the determined similarityrelationship, that the specific candidate security threat target willexperience a future security threat attack, and (5) perform, by thesoftware security prediction program, at least one remedial action toprotect the specific candidate security threat target in response topredicting the future security threat attack.

Features from any of the above-mentioned embodiments may be used incombination with one another in accordance with the general principlesdescribed herein. These and other embodiments, features, and advantageswill be more fully understood upon reading the following detaileddescription in conjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate a number of exemplary embodimentsand are a part of the specification. Together with the followingdescription, these drawings demonstrate and explain various principlesof the instant disclosure.

FIG. 1 is a block diagram of an exemplary system for predicting securitythreat attacks.

FIG. 2 is a block diagram of an additional exemplary system forpredicting security threat attacks.

FIG. 3 is a flow diagram of an exemplary method for predicting securitythreat attacks.

FIG. 4 is a block diagram of an exemplary matrix referenced by exemplarymethods and systems for predicting security threat attacks.

FIG. 5 is a block diagram of an exemplary computing system capable ofimplementing one or more of the embodiments described and/or illustratedherein.

FIG. 6 is a block diagram of an exemplary computing network capable ofimplementing one or more of the embodiments described and/or illustratedherein.

Throughout the drawings, identical reference characters and descriptionsindicate similar, but not necessarily identical, elements. While theexemplary embodiments described herein are susceptible to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and will be described in detailherein. However, the exemplary embodiments described herein are notintended to be limited to the particular forms disclosed. Rather, theinstant disclosure covers all modifications, equivalents, andalternatives falling within the scope of the appended claims.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present disclosure is generally directed to systems and methods forpredicting security threat attacks. As will be explained in greaterdetail below, the disclosed systems and methods may enable and/orimprove the prediction of security threat attacks by newly applying oneor more algorithms to historical attack data that describes histories ofattacks on various security targets, such as enterprise organizations.For example, the disclosed systems and methods may apply a collaborativefiltering algorithm to improve predictions of security threat attacks,thereby enabling predicted targets to take preventive action prior tothe attacks occurring.

The following will provide, with reference to FIGS. 1-2, detaileddescriptions of exemplary systems for predicting security threatattacks. Detailed descriptions of corresponding computer-implementedmethods will also be provided in connection with FIGS. 3-4. In addition,detailed descriptions of an exemplary computing system and networkarchitecture capable of implementing one or more of the embodimentsdescribed herein will be provided in connection with FIGS. 5 and 6,respectively.

FIG. 1 is a block diagram of exemplary system 100 for predictingsecurity threat attacks. As illustrated in this figure, exemplary system100 may include one or more modules 102 for performing one or moretasks. For example, and as will be explained in greater detail below,exemplary system 100 may also include an identification module 104 thatmay identify candidate security threat targets with latent attributesthat describe features of the candidate security threat targets.Identification module 104 may also identify historical attack data thatdescribes which of the candidate security threat targets experienced anactual security threat attack.

Additionally, exemplary system 100 may include a determination module106 that may determine, as part of a software security predictionprogram, a similarity relationship between latent attributes of at leastone specific candidate security threat target and latent attributes ofthe candidate security threat targets that experienced an actualsecurity threat attack according to the historical attack data.Exemplary system 100 may also include a prediction module 108 that maypredict, as part of the software security prediction program based onthe determined similarity relationship, that the specific candidatesecurity threat target will experience a future security threat attack.

Exemplary system 100 may additionally include a performance module 110that may perform, as part of the software security prediction program,at least one remedial action to protect the specific candidate securitythreat target in response to predicting the future security threatattack. Although illustrated as separate elements, one or more ofmodules 102 in FIG. 1 may represent portions of a single module orapplication.

In certain embodiments, one or more of modules 102 in FIG. 1 mayrepresent one or more software applications or programs that, whenexecuted by a computing device, may cause the computing device toperform one or more tasks. For example, and as will be described ingreater detail below, one or more of modules 102 may represent softwaremodules stored and configured to run on one or more computing devices,such as the devices illustrated in FIG. 2 (e.g., computing device 202and/or server 206), computing system 510 in FIG. 5, and/or portions ofexemplary network architecture 600 in FIG. 6. One or more of modules 102in FIG. 1 may also represent all or portions of one or morespecial-purpose computers configured to perform one or more tasks.

As illustrated in FIG. 1, exemplary system 100 may also include one ormore databases, such as database 120. In one example, database 120 maybe configured to store historical attack data 122, which may indicateinformation about candidate security threat targets, such as enterpriseorganizations, and security threat attacks, such as malware attacks,that actually occurred. For example, historical attack data 122 mayidentify the enterprise organization, the instance or type of malwareattack, the timing of the malware attack or detection, and/or any otherassociated metadata that describes the enterprise organization or theassociated malware. Database 120 may also be configured to storematrices 124, which may map security threat targets to correspondingsecurity threat attacks, as discussed further below in connection withFIG. 4.

Database 120 may represent portions of a single database or computingdevice or a plurality of databases or computing devices. For example,database 120 may represent a portion of server 206 in FIG. 2, computingsystem 510 in FIG. 5, and/or portions of exemplary network architecture600 in FIG. 6. Alternatively, database 120 in FIG. 1 may represent oneor more physically separate devices capable of being accessed by acomputing device, such as server 206 in FIG. 2, computing system 510 inFIG. 5, and/or portions of exemplary network architecture 600 in FIG. 6.

Exemplary system 100 in FIG. 1 may be implemented in a variety of ways.For example, all or a portion of exemplary system 100 may representportions of exemplary system 200 in FIG. 2. As shown in FIG. 2, system200 may include a computing device 202 in communication with a server206 via a network 204. In one example, computing device 202 may beprogrammed with one or more of modules 102 and/or may store all or aportion of the data in database 120. Additionally or alternatively,server 206 may be programmed with one or more of modules 102 and/or maystore all or a portion of the data in database 120.

In one embodiment, one or more of modules 102 from FIG. 1 may, whenexecuted by at least one processor of computing device 202 and/or server206, enable computing device 202 and/or server 206 to predict securitythreat attacks. For example, and as will be described in greater detailbelow, identification module 104 may identify candidate security threattargets, such as an enterprise organization 220 and an enterpriseorganization 222, with latent attributes that describe features of thecandidate security threat targets. Identification module 104 may alsoidentify historical attack data, such as historical attack data 122,that describes which of the candidate security threat targetsexperienced an actual security threat attack. In the example of thisfigure, a computing device 203 (which may parallel computing device 202)within enterprise organization 222 previously detected a malware 240 anda malware 242. Similarly, computing device 202 within enterpriseorganization 220 previously detected malware 240 but has not yetdetected the presence of malware 242 (as indicated by the dashed lines).

Determination module 106 may determine, as part of a software securityprediction program or system, a similarity relationship 232 betweenlatent attributes of at least one specific candidate security threattarget (e.g., enterprise organization 220) and latent attributes of thecandidate security threat targets (e.g., including enterpriseorganization 222) that experienced an actual security threat attackaccording to the historical attack data. For example, enterpriseorganization 222 experienced actual attacks associated with malware 240and malware 242.

Prediction module 108 may predict, as part of the software securityprediction program or system based on determined similarity relationship232, that enterprise organization 220 will experience a future securitythreat attack. In this specific example, prediction module 108 maypredict that enterprise organization 220 will experience a futuresecurity threat attack associated with malware 242, which is not yetdetected at enterprise organization 220. Accordingly, performance module110 may perform, as part of the software security prediction program orsystem, at least one remedial action to protect enterprise organization220 in response to predicting the future security threat attack. Forexample, performance module 110 may issue a notification 230 toenterprise organization 220 to notify or warn enterprise organization220 about the predicted future attack. Notably, FIG. 2 also illustrateshow enterprise organization 220 and enterprise organization 222 bothstored a benign cluster 244 of files, as discussed below regarding step306 in connection with FIG. 3.

Computing device 202 generally represents any type or form of computingdevice capable of reading computer-executable instructions. Examples ofcomputing device 202 include, without limitation, laptops, tablets,desktops, servers, cellular phones, Personal Digital Assistants (PDAs),multimedia players, embedded systems, wearable devices (e.g., smartwatches, smart glasses, etc.), gaming consoles, combinations of one ormore of the same, exemplary computing system 510 in FIG. 5, or any othersuitable computing device.

Server 206 generally represents any type or form of computing devicethat is capable of facilitating the prediction of security threatattacks, as discussed below. Examples of server 206 include, withoutlimitation, application servers and database servers configured toprovide various database services and/or run certain softwareapplications.

Network 204 generally represents any medium or architecture capable offacilitating communication or data transfer. Examples of network 204include, without limitation, an intranet, a Wide Area Network (WAN), aLocal Area Network (LAN), a Personal Area Network (PAN), the Internet,Power Line Communications (PLC), a cellular network (e.g., a GlobalSystem for Mobile Communications (GSM) network), exemplary networkarchitecture 600 in FIG. 6, or the like. Network 204 may facilitatecommunication or data transfer using wireless or wired connections. Inone embodiment, network 204 may facilitate communication betweencomputing device 202 and server 206.

FIG. 3 is a flow diagram of an exemplary computer-implemented method 300for predicting security threat attacks. The steps shown in FIG. 3 may beperformed by any suitable computer-executable code and/or computingsystem. In some embodiments, the steps shown in FIG. 3 may be performedby one or more of the components of system 100 in FIG. 1, system 200 inFIG. 2, computing system 510 in FIG. 5, and/or portions of exemplarynetwork architecture 600 in FIG. 6.

As illustrated in FIG. 3, at step 302, one or more of the systemsdescribed herein may identify candidate security threat targets withlatent attributes that describe features of the candidate securitythreat targets. For example, identification module 104 may, as part ofserver 206 in FIG. 2, identify enterprise organization 220 andenterprise organization 222 with latent attributes that describefeatures of these organizations.

As used herein, the term “candidate security threat target” generallyrefers to any entity that may be targets for computer-based securityattacks, such as malware attacks. Examples of candidate security threattargets include computing systems, computing devices, network devices,enterprise organizations, legal entities, organizations, businesses,individuals, product lines, financial assets or accounts, and/or anypermutation of these, etc. Moreover, candidate security threat targetsmay be grouped together for analysis, such as a set of candidatesecurity threat targets that share a specific attribute, type, modelnumber, product line, characteristic, etc. Moreover, as used herein, theterm “latent attributes” generally refers to any attribute of acandidate security threat target that a security or prediction analysismay ascertain as relevant to predicting future security threat attacks,as discussed further below. In some examples, latent attributes maysimply refer to data indicating historical security threat attacksand/or a pattern or timing of security threat attacks. In otherexamples, latent attributes may be revealed through a collaborativefiltering and/or matrix factorization analysis, as discussed furtherbelow in connection with FIG. 4.

Identification module 104 may identify the candidate security threattargets in a variety of ways. In one embodiment, the candidate securitythreat targets include enterprise organizations, such as enterpriseorganization 220 and enterprise organization 222. In further examples,the enterprise organizations include customers of a vendor of thesoftware security prediction program, which may correspond to system 200and/or server 206. In other words, enterprise customers of a softwaresecurity vendor may purchase predictive analysis to predict whether thecustomers will become targets of future attacks. Accordingly, thesoftware security vendor may collect or obtain historical attack data122 that indicates previously detected attacks directed to the customersand/or directed to other targets. A backend security server, such asserver 206, may analyze historical attack data 122 to predict which ofthe customers will become targets of actual attacks and also optionallypredict the identity, timing, and/or nature of the attacks, as discussedfurther below.

In general, identification module 104 may identify the candidatesecurity threat targets in response to a request to purchase aprediction analysis that predicts whether and/or how the candidatesecurity threat targets will become actual targets of future attacks.Identification module 104 may also identify the candidate securitythreat targets in any other suitable manner, such as by analyzingpublicly available data and/or as part of providing complementary ortrial-based security services.

Moreover, to reduce the size of the data set and/or correspondingmatrix, identification module 104 may aggregate targets into groups oftargets. For example, identification module 104 may aggregate clientdevices or machines into groups of client devices or into correspondingenterprise organizations. Similarly, identification module 104 mayaggregate malware samples into malware families (e.g., as determined bya malware index/database or software security product provided by asoftware security vendor) or into even broader categories of threats,such as adware, trojans, targeted attacks, etc.

At step 304, one or more of the systems described herein may identifyhistorical attack data that describes which of the candidate securitythreat targets experienced an actual security threat attack. Forexample, identification module 104 may, as part of server 206 in FIG. 2,identify historical attack data 122 that describes which of enterpriseorganization 220 and enterprise organization 222 (e.g., among otherenterprise organizations) experienced an actual security threat attack.

Identification module 104 may identify the historical attack data in avariety of ways. As discussed above, identification module 104 mayidentify the historical attack data as part of a purchase agreement forpurchasing prediction services, as outlined above. In other words, acustomer of a software security vendor may provide historical attackdata as part of the purchase agreement to enable the software securityvendor to perform the prediction analysis. Additionally, oralternatively, the software security vendor may have obtained historicalattack data prior to the purchase agreement. For example, the softwaresecurity vendor may have obtained historical attack data through aprevious purchase of a different security product or service, such asantivirus or intrusion prevention products. Similarly, the softwaresecurity vendor may have obtained historical attack data throughcomplementary or trial-based services, such as a free antivirus program.In general, one or more software security products, such as antivirusprograms, may be freely available and widely in use, and these softwaresecurity products may broadly collect telemetry data on historicalattacks at corresponding client devices and associated enterpriseorganizations.

At step 306, one or more of the systems described herein may determine,as part of a software security prediction program, a similarityrelationship between latent attributes of at least one specificcandidate security threat target and latent attributes of the candidatesecurity threat targets that experienced an actual security threatattack according to the historical attack data. For example,determination module 106 may, as part of server 206 in FIG. 2, determinea similarity relationship between latent attributes of enterpriseorganization 220 and latent attributes of other enterprise organizations(e.g., including enterprise organization 222) that experienced an actualsecurity threat attack according to historical attack data 122.

As used herein, the term “similarity relationship” generally refers toany relationship, mathematical formula, result of a mathematicalformula, and/or measured association that enables a prediction serviceor product to predict a future attack on one entity based on analysis ofdata indicating corresponding attacks on other or related entities. Insome examples, the similarity relationship indicates that the candidatesecurity threat targets share one or more attributes, which may havepredictive power according to the prediction analysis, as discussedfurther below. In other examples, the similarity relationship may simplycorrespond to a matrix factorization that generates two matricesenabling a prediction service or product to predict a future securityattack based on an analysis of previous attacks on other or relatedentities.

Determination module 106 may determine the similarity relationship in avariety of ways. In one embodiment, determination module 106 maydetermine the similarity relationship by (1) identifying an additionalcandidate security threat target that experienced a pair of actualsecurity threat attacks and (2) determining that the specific candidatesecurity threat target experienced one of the pair of actual securitythreat attacks. In the example of FIG. 2, determination module 106 mayidentify enterprise organization 222 that experienced the pair of actualsecurity threat attacks corresponding to malware 240 and malware 242.Determination module 106 may also determine that enterprise organization220 experienced one of the pair of these security threat attacks (i.e.,experienced malware 240). Accordingly, prediction module 108 may predictthat the specific candidate security threat target (e.g., enterpriseorganization 220) will experience a future security threat attack (i.e.,malware 242) based on this analysis. In this simplified example,prediction module 108 may predict the future malware attack for malware242 based simply on the analysis of historical attacks on enterpriseorganization 222. Nevertheless, in other examples, prediction module 108may predict the future malware attack form malware 242 at enterpriseorganization 220 based on an analysis of multiple numerous otherenterprise organizations. For example, determination module 106 maydetermine that a threshold number of other enterprise organizationsexperienced both malware 240 and malware 242 and/or experienced malware242 after previously experiencing malware 240 (e.g., as determined bydetecting a download, installation, presence, and/or activity associatedwith the corresponding malware).

In some examples, determination module 106 may determine the similarityrelationship by identifying an additional candidate security threattarget that stored a cluster of benign files and that experienced a samesecurity threat attack as the predicted future security threat attack.For example, determination module 106 may identify enterpriseorganization 222 that stored benign cluster 244 and that experiencedmalware 240, which enterprise organization 220 also experienced, asdiscussed above. In these examples, determination module 106 may furtherdetermine that the specific candidate security threat target also storedthe cluster of benign files. More specifically, determination module 106may further determine that enterprise organization 220 also storedbenign cluster 244. The fact that both enterprise organization 220 andenterprise organization 222 both stored benign cluster 244 mayconstitute a similarity relationship according to which predictionmodule 108 may predict that enterprise organization 220 will alsoexperience malware 242. In other words, prediction module 108 may basethe prediction on an estimated likelihood that organizations that storesimilar files or clusters of files will also be correspondingly morelikely to experience the same or similar security threat attacks.

In further examples, determination module 106 may determine thesimilarity relationship by analyzing a matrix that identifies (1)enterprise organizations corresponding to at least one of rows andcolumns of the matrix and (2) security threat attacks corresponding tothe other of the rows and columns of the matrix. FIG. 4 shows an exampleof such a matrix, matrix 400. As shown in this figure, matrix 400 mayidentify enterprise organization 220, enterprise organization 222, anenterprise organization 402, an enterprise organization 404, anenterprise organization 406, and an enterprise organization 408corresponding to rows. Similarly, matrix 400 may identify malware 240,malware 242, a malware 410, and a malware 412 corresponding to columns.Matrix 400 further shows that enterprise organization 220 detectedmalware 240 and that enterprise organization 222 detected malware 240and malware 242, as discussed above in connection with FIG. 2. Matrix400 also shows other detected malware at other specified enterpriseorganizations. Notably, matrix 400 also includes a question mark at theintersection of enterprise organization 220 and malware 242, indicatingthat malware 242 has not yet been detected at enterprise organization220, yet prediction module 108 may predict that enterprise organization220 will detect the corresponding attack in the future (e.g., in theabsence of remedial or preventive measures to prevent the attack).

In one embodiment, the matrix may include a sparse matrix, such as asparse binary matrix. For example, matrix 400 includes mostly zeros orblank space, with a relatively small proportion of the matrix entriesincluding a “1” to indicate a known or detected previous malware attack.Notably, although the enterprise organizations correspond to rows andthe malware instances correspond to columns in FIG. 4, the disclosedsystems and methods may also analyze matrices that reverse thisorientation (e.g., the corresponding mathematical operations areappropriately symmetrical).

In further examples, determination module 106 may determine thesimilarity relationship by performing a rank factorization (e.g., a lowrank factorization or collaborative filtering computation) of thematrix. For example, determination module 106 may factor matrix 400 intoa matrix 426 and a matrix 434, as further shown in FIG. 4. Notably,matrix 426 may include a same number of rows as matrix 400, whichcorresponds to the same six enterprise organizations. Similarly, matrix434 may include a same number of columns as matrix 400, whichcorresponds to the same four malware instances, types, families, and/oridentifiers.

Furthermore, matrix 426 may indicate a factor 420 and a factor 422 ascolumns, whereas matrix 434 may indicate a factor 430 and a factor 432as rows. Each of these factors may simply correspond to a type orgrouping of numerical values (e.g., numerical values along the columnsin matrix 426 and along the rows in matrix 434) that, when multipliedaccording to matrix or vector multiplication, produce the same orapproximately the same values shown in matrix 400. Notably, althoughmatrix 400 includes mostly blank space, multiplying correspondingvectors from matrix 426 and matrix 434 may produce values for each entrywithin matrix 400, thereby filling the blank space by predicting valuesindicating whether the corresponding enterprise organization and malware(as indicated by an intersection at matrix 400) will experience a futuresecurity threat attack.

In the example of FIG. 4, the matrix factorization may result in twomatrices that reproduce the same or approximately the same matrix 400based on two latent factors at each of matrix 426 and matrix 434.Detecting the numerical values for the latent factors for eachenterprise organization and for each malware instance will therebycreate or generate a short real vector as the latent representation ofthe respective enterprise organization and/or malware instance.Moreover, prediction module 108 may generate a prediction for each pairof enterprise organization and malware instance by computing the innerproduct of their latent representation vectors.

Nevertheless, the number of factors at each factored matrix may be amatter of design choice (although the number of factors must generallybe the same between the two factored matrices, such as the two factorsat each of matrix 426 and matrix 434, according to matrixfactorization). A smaller number of factors may produce less accuracy inreproducing matrix 400 but may be fast and/or computationallyinexpensive. In contrast, a larger number of factors may produce moreaccuracy in reproducing matrix 400 but maybe slower and/orcomputationally more expensive and intractable. In some examples,determination module 106 may factor matrix 400 a multitude of times,each time testing to determine a number of factors that results in anappropriate balance between accuracy in reproducing matrix 400 andcomputational efficiency or tractability (e.g., according to apredefined formula or threshold indicating an appropriate balance ortrade-off).

In further examples, to address challenges with computationalintractability (e.g., with sufficiently large matrices and/or datasets), determination module 106 may perform the matrix factorization atleast in part by executing a stochastic gradient descent algorithm. Forexample, determination module 106 may randomly sample entries or areasof matrix 400 and solve one or more surrounding entries or areasaccording to a stochastic gradient descent algorithm, thereby enablingthe matrix factorization to more efficiently factor matrix 400 andthereby improve the computational tractability of the matrixfactorization.

More specifically, determination module 106 may execute the stochasticgradient descent algorithm in parallel using parallel computerprocessing, thereby shortening the computing time of the entire system.In these examples, each iteration of the stochastic gradient descentalgorithm picks a random observed matrix entry and another number (e.g.,ten to twenty) of unobserved matrix entries as a small subset of thetraining data. Determination module 106 may then compute gradients onthe subsample and then determination module 106 may update modelparameters using regular gradient descent. Since the data matrix isextremely sparse, determination module 106 may significantly down-weighteach negative unobserved matrix entry to compensate for the sparsenessof observed matrix entries. In other examples, determination module 106may address challenges with computational intractability in part byperforming the matrix factorization according to an alternatingleast-squares algorithm.

In further examples, determination module 106 may determine thesimilarity relationship by (1) ranking candidate security threat targetsin terms of counts of experiencing actual security threat attacks and(2) ranking security threat attacks in terms of actually attackingenterprise organizations. For example, determination module 106 may rankone or more enterprise organizations in terms of counts of experiencingactual security threat attacks. In the example of FIG. 4, enterpriseorganization 408 is ranked first because enterprise organization 408experienced three different malware attacks, which is more than theother enterprise organizations. Similarly, malware 240 is ranked firstbecause malware 240 attacked three separate enterprise organizations,which is more than the other malware instances.

In view of the above, determination module 106 may determine thesimilarity relationship in part by predicting that malware 240, as themost aggressive or popular malware, will attack enterprise organization408, as the most vulnerable enterprise organization (e.g., determinethat enterprise organization 408 has a similarity relationship with theother enterprise organizations that experienced malware 240).Determination module 106 may similarly determine similarityrelationships for other enterprise organizations going down the ranks ofthe most popular malware and/or the most vulnerable enterpriseorganizations, thereby enabling prediction module 108 to predict futureattacks up to a predefined cutoff or threshold in terms of malwareand/or enterprise rank. In other words, prediction module 108 maypredict that the specific candidate security threat target willexperience a future security threat attack based on the rank of thespecific candidate security threat target and/or the rank of thepredicted future security threat attack.

At step 308, one or more of the systems described herein may predict, aspart of the software security prediction program based on the determinedsimilarity relationship, that the specific candidate security threattarget will experience a future security threat attack. For example,prediction module 108 may, as part of server 206 in FIG. 2, predict,based on the determined similarity relationship, that the specificcandidate security threat target will experience a future securitythreat attack.

Prediction module 108 may predict the future security threat attack in avariety of ways. In general, prediction module 108 may predict thefuture security threat attack in accordance with any of the predictionanalyses and/or similarity relationship determinations outlined above inconnection with step 306 of method 300. In other words, predictionmodule 108 may predict the future security threat attack using anysuitable recommendations system, content filtering recommendationsystem, collaborative filtering recommendation system, neighborhoodmethod, latent factor model, matrix factorization, low rank matrixfactorization, stochastic gradient descent algorithm, and/or alternatingleast-squares algorithm, as outlined above, to recommend predictedfuture attacks for candidate targets based on an analysis of historicaldata for previous attacks on one or more other targets.

In other words, the disclosed systems and methods may leverage andreengineer recommendation systems, such as recommendation systems usedto recommend movies, music, and/or products (e.g., recommendationsystems used by NETFLIX and/or AMAZON) to recommend predictions forfuture malware or other security attacks instead of recommending mediacontent or products. For example, media vendors such as NETFLIX apply afriend-of-a-friend algorithm to recommend media content to one friendbased on a determination that a friend of the friend also liked themedia content. Accordingly, by analogy, the disclosed systems andmethods may predict a future security threat attack for one candidatetarget based on a determination that the candidate target has asimilarity relationship (e.g., shares one or more attributes, asdiscussed above in connection with FIGS. 2 and 4) with a previous actualtarget of the same or related security threat attack.

In the example of FIG. 4, prediction module 108 may predict the futuresecurity threat attack by determining that a previously unknown or blankentry for matrix 400, when populated by multiplying matrix 426 andmatrix 434 (e.g., by multiplying a vector of matrix 426 by acorresponding vector of matrix 434), receives a value that satisfies apredetermined threshold or metric indicating a predicted future attack(e.g., a value that satisfies a predetermined threshold or metricindicating sufficient nearness to the value “1” for other previouslydetected attacks). In other examples, the previously unknown or blankentry for matrix 400 may be populated by the exact value of “1” when acorresponding attack is predicted. In this manner, prediction module 108may attempt to partially or entirely complete matrix 400, therebypredicting future attacks based on data indicating previous attacks.

Moreover, in completing matrix 400, prediction module 108 may alsoincorporate, or factor in, side information, such as software packagesinstalled on testing machines and virus categories reported by asoftware security vendor for malicious executables, thereby improvingprediction accuracy. For example, prediction module 108 may retrievefile clusters on enterprise machines as well as categories and filenames of malicious executable files by querying a software securityvendor database. Prediction module 108 may then construct featurevectors for each enterprise and malicious file instance. The featurevectors provide more details of each row and column of the incompletedata matrix. Accordingly, the feature vectors may help predict potentialattacks for those enterprises whose previous attack history is limited.Mathematically, prediction module 108 may incorporate side information(e.g., feature vectors) into the prediction by augmenting theconventional low-rank collaborative filtering model with an additionallow rank predicting matrix.

In one specific example, suppose x and y are feature vectors of aspecific (enterprise, malware) pair. The proposed algorithm fits alow-dimensional prediction matrix A and defines the prediction score ofa given enterprise-malware pair by computing the bi-linear form betweenthe fitted prediction matrix A and the feature vectors x and y. Finally,prediction module 108 may combine predictions obtained by sideinformation with predictions made by collaborative filtering to makefinal recommendations through performance module 110, as discussedfurther below. More generally, prediction module 108 and/ordetermination module 106 may attempt to ascertain the attributes thatmake a candidate target normal, or more normal according to astatistical or mathematical analysis, and then find correlations betweenthat measured level of normalcy and susceptibility to specific securitythreat attacks.

At step 310, one or more of the systems described herein may perform, aspart of the software security prediction program, at least one remedialaction to protect the specific candidate security threat target inresponse to predicting the future security threat attack. For example,performance module 110 may, as part of server 206 in FIG. 2, perform atleast one remedial action to protect enterprise organization 220 inresponse to predicting the future security threat attack.

As used herein, the term “remedial action” generally refers to anyaction that an autonomous, semi-autonomous, automated, and/or manuallyoperated software security prediction program may take to help protect acandidate security threat target. Examples of the remedial action mayinclude notifying the predicted target, notifying a user and/oradministrator (e.g., notifying one or more individuals of anyinformation about the attack, including an identity, nature, timing,and/or recommended protective action associated with the attack),prompting the user and/or administrator to take one or more additionalremedial actions, and/or enabling, strengthening, and/or heightening oneor more security measures. The security measures may include antivirus,intrusion prevention system, firewall, virtual private networking,sandboxing, quarantining, virtualization, and/or data loss preventionmeasures. In some examples, the remedial action may be targeted ortailored to the specific instance, type, identifier, hash, family,and/or cluster of malware or other security threat predicted to attackthe corresponding target.

In some examples, the remedial action may include updating a securityproduct definition or signature set to include a definition or signaturefor the predicted security threat attack, thereby enabling acorresponding security product to identify and neutralize the predictedattack. Similarly, the remedial action may include updating a securityprogram set of scripts for removing, uninstalling, and/or otherwiseneutralizing or inhibiting corresponding security threat attacks. Otherexamples of the remedial action may also include targeted employeetraining, assigning a security risk score to the target, selectivesystem hardening, and/or customized honey traps.

As explained above in connection with method 300 in FIG. 3, thedisclosed systems and methods may enable and/or improve the predictionof security threat attacks by newly applying one or more algorithms tohistorical attack data that describes histories of attacks on varioussecurity targets, such as enterprise organizations. For example, thedisclosed systems and methods may apply a collaborative filteringalgorithm to improve predictions of security threat attacks, therebyenabling predicted targets to take preventive action prior to theattacks occurring.

FIG. 5 is a block diagram of an exemplary computing system 510 capableof implementing one or more of the embodiments described and/orillustrated herein. For example, all or a portion of computing system510 may perform and/or be a means for performing, either alone or incombination with other elements, one or more of the steps describedherein (such as one or more of the steps illustrated in FIG. 3). All ora portion of computing system 510 may also perform and/or be a means forperforming any other steps, methods, or processes described and/orillustrated herein.

Computing system 510 broadly represents any single or multi-processorcomputing device or system capable of executing computer-readableinstructions. Examples of computing system 510 include, withoutlimitation, workstations, laptops, client-side terminals, servers,distributed computing systems, handheld devices, or any other computingsystem or device. In its most basic configuration, computing system 510may include at least one processor 514 and a system memory 516.

Processor 514 generally represents any type or form of physicalprocessing unit (e.g., a hardware-implemented central processing unit)capable of processing data or interpreting and executing instructions.In certain embodiments, processor 514 may receive instructions from asoftware application or module. These instructions may cause processor514 to perform the functions of one or more of the exemplary embodimentsdescribed and/or illustrated herein.

System memory 516 generally represents any type or form of volatile ornon-volatile storage device or medium capable of storing data and/orother computer-readable instructions. Examples of system memory 516include, without limitation, Random Access Memory (RAM), Read OnlyMemory (ROM), flash memory, or any other suitable memory device.Although not required, in certain embodiments computing system 510 mayinclude both a volatile memory unit (such as, for example, system memory516) and a non-volatile storage device (such as, for example, primarystorage device 532, as described in detail below). In one example, oneor more of modules 102 from FIG. 1 may be loaded into system memory 516.

In certain embodiments, exemplary computing system 510 may also includeone or more components or elements in addition to processor 514 andsystem memory 516. For example, as illustrated in FIG. 5, computingsystem 510 may include a memory controller 518, an Input/Output (I/O)controller 520, and a communication interface 522, each of which may beinterconnected via a communication infrastructure 512. Communicationinfrastructure 512 generally represents any type or form ofinfrastructure capable of facilitating communication between one or morecomponents of a computing device. Examples of communicationinfrastructure 512 include, without limitation, a communication bus(such as an Industry Standard Architecture (ISA), Peripheral ComponentInterconnect (PCI), PCI Express (PCIe), or similar bus) and a network.

Memory controller 518 generally represents any type or form of devicecapable of handling memory or data or controlling communication betweenone or more components of computing system 510. For example, in certainembodiments memory controller 518 may control communication betweenprocessor 514, system memory 516, and I/O controller 520 viacommunication infrastructure 512.

I/O controller 520 generally represents any type or form of modulecapable of coordinating and/or controlling the input and outputfunctions of a computing device. For example, in certain embodiments I/Ocontroller 520 may control or facilitate transfer of data between one ormore elements of computing system 510, such as processor 514, systemmemory 516, communication interface 522, display adapter 526, inputinterface 530, and storage interface 534.

Communication interface 522 broadly represents any type or form ofcommunication device or adapter capable of facilitating communicationbetween exemplary computing system 510 and one or more additionaldevices. For example, in certain embodiments communication interface 522may facilitate communication between computing system 510 and a privateor public network including additional computing systems. Examples ofcommunication interface 522 include, without limitation, a wired networkinterface (such as a network interface card), a wireless networkinterface (such as a wireless network interface card), a modem, and anyother suitable interface. In at least one embodiment, communicationinterface 522 may provide a direct connection to a remote server via adirect link to a network, such as the Internet. Communication interface522 may also indirectly provide such a connection through, for example,a local area network (such as an Ethernet network), a personal areanetwork, a telephone or cable network, a cellular telephone connection,a satellite data connection, or any other suitable connection.

In certain embodiments, communication interface 522 may also represent ahost adapter configured to facilitate communication between computingsystem 510 and one or more additional network or storage devices via anexternal bus or communications channel. Examples of host adaptersinclude, without limitation, Small Computer System Interface (SCSI) hostadapters, Universal Serial Bus (USB) host adapters, Institute ofElectrical and Electronics Engineers (IEEE) 1394 host adapters, AdvancedTechnology Attachment (ATA), Parallel ATA (PATA), Serial ATA (SATA), andExternal SATA (eSATA) host adapters, Fibre Channel interface adapters,Ethernet adapters, or the like. Communication interface 522 may alsoallow computing system 510 to engage in distributed or remote computing.For example, communication interface 522 may receive instructions from aremote device or send instructions to a remote device for execution.

As illustrated in FIG. 5, computing system 510 may also include at leastone display device 524 coupled to communication infrastructure 512 via adisplay adapter 526. Display device 524 generally represents any type orform of device capable of visually displaying information forwarded bydisplay adapter 526. Similarly, display adapter 526 generally representsany type or form of device configured to forward graphics, text, andother data from communication infrastructure 512 (or from a framebuffer, as known in the art) for display on display device 524.

As illustrated in FIG. 5, exemplary computing system 510 may alsoinclude at least one input device 528 coupled to communicationinfrastructure 512 via an input interface 530. Input device 528generally represents any type or form of input device capable ofproviding input, either computer or human generated, to exemplarycomputing system 510. Examples of input device 528 include, withoutlimitation, a keyboard, a pointing device, a speech recognition device,or any other input device.

As illustrated in FIG. 5, exemplary computing system 510 may alsoinclude a primary storage device 532 and a backup storage device 533coupled to communication infrastructure 512 via a storage interface 534.Storage devices 532 and 533 generally represent any type or form ofstorage device or medium capable of storing data and/or othercomputer-readable instructions. For example, storage devices 532 and 533may be a magnetic disk drive (e.g., a so-called hard drive), a solidstate drive, a floppy disk drive, a magnetic tape drive, an optical diskdrive, a flash drive, or the like. Storage interface 534 generallyrepresents any type or form of interface or device for transferring databetween storage devices 532 and 533 and other components of computingsystem 510. In one example, database 120 from FIG. 1 may be stored inprimary storage device 532.

In certain embodiments, storage devices 532 and 533 may be configured toread from and/or write to a removable storage unit configured to storecomputer software, data, or other computer-readable information.Examples of suitable removable storage units include, withoutlimitation, a floppy disk, a magnetic tape, an optical disk, a flashmemory device, or the like. Storage devices 532 and 533 may also includeother similar structures or devices for allowing computer software,data, or other computer-readable instructions to be loaded intocomputing system 510. For example, storage devices 532 and 533 may beconfigured to read and write software, data, or other computer-readableinformation. Storage devices 532 and 533 may also be a part of computingsystem 510 or may be a separate device accessed through other interfacesystems.

Many other devices or subsystems may be connected to computing system510. Conversely, all of the components and devices illustrated in FIG. 5need not be present to practice the embodiments described and/orillustrated herein. The devices and subsystems referenced above may alsobe interconnected in different ways from that shown in FIG. 5. Computingsystem 510 may also employ any number of software, firmware, and/orhardware configurations. For example, one or more of the exemplaryembodiments disclosed herein may be encoded as a computer program (alsoreferred to as computer software, software applications,computer-readable instructions, or computer control logic) on acomputer-readable medium. The phrase “computer-readable medium,” as usedherein, generally refers to any form of device, carrier, or mediumcapable of storing or carrying computer-readable instructions. Examplesof computer-readable media include, without limitation,transmission-type media, such as carrier waves, and non-transitory-typemedia, such as magnetic-storage media (e.g., hard disk drives, tapedrives, and floppy disks), optical-storage media (e.g., Compact Disks(CDs), Digital Video Disks (DVDs), and BLU-RAY disks),electronic-storage media (e.g., solid-state drives and flash media), andother distribution systems.

The computer-readable medium containing the computer program may beloaded into computing system 510. All or a portion of the computerprogram stored on the computer-readable medium may then be stored insystem memory 516 and/or various portions of storage devices 532 and533. When executed by processor 514, a computer program loaded intocomputing system 510 may cause processor 514 to perform and/or be ameans for performing the functions of one or more of the exemplaryembodiments described and/or illustrated herein. Additionally oralternatively, one or more of the exemplary embodiments described and/orillustrated herein may be implemented in firmware and/or hardware. Forexample, computing system 510 may be configured as an ApplicationSpecific Integrated Circuit (ASIC) adapted to implement one or more ofthe exemplary embodiments disclosed herein.

FIG. 6 is a block diagram of an exemplary network architecture 600 inwhich client systems 610, 620, and 630 and servers 640 and 645 may becoupled to a network 650. As detailed above, all or a portion of networkarchitecture 600 may perform and/or be a means for performing, eitheralone or in combination with other elements, one or more of the stepsdisclosed herein (such as one or more of the steps illustrated in FIG.3). All or a portion of network architecture 600 may also be used toperform and/or be a means for performing other steps and features setforth in the instant disclosure.

Client systems 610, 620, and 630 generally represent any type or form ofcomputing device or system, such as exemplary computing system 510 inFIG. 5. Similarly, servers 640 and 645 generally represent computingdevices or systems, such as application servers or database servers,configured to provide various database services and/or run certainsoftware applications. Network 650 generally represents anytelecommunication or computer network including, for example, anintranet, a WAN, a LAN, a PAN, or the Internet. In one example, clientsystems 610, 620, and/or 630 and/or servers 640 and/or 645 may includeall or a portion of system 100 from FIG. 1.

As illustrated in FIG. 6, one or more storage devices 660(1)-(N) may bedirectly attached to server 640. Similarly, one or more storage devices670(1)-(N) may be directly attached to server 645. Storage devices660(1)-(N) and storage devices 670(1)-(N) generally represent any typeor form of storage device or medium capable of storing data and/or othercomputer-readable instructions. In certain embodiments, storage devices660(1)-(N) and storage devices 670(1)-(N) may represent Network-AttachedStorage (NAS) devices configured to communicate with servers 640 and 645using various protocols, such as Network File System (NFS), ServerMessage Block (SMB), or Common Internet File System (CIFS).

Servers 640 and 645 may also be connected to a Storage Area Network(SAN) fabric 680. SAN fabric 680 generally represents any type or formof computer network or architecture capable of facilitatingcommunication between a plurality of storage devices. SAN fabric 680 mayfacilitate communication between servers 640 and 645 and a plurality ofstorage devices 690(1)-(N) and/or an intelligent storage array 695. SANfabric 680 may also facilitate, via network 650 and servers 640 and 645,communication between client systems 610, 620, and 630 and storagedevices 690(1)-(N) and/or intelligent storage array 695 in such a mannerthat devices 690(1)-(N) and array 695 appear as locally attached devicesto client systems 610, 620, and 630. As with storage devices 660(1)-(N)and storage devices 670(1)-(N), storage devices 690(1)-(N) andintelligent storage array 695 generally represent any type or form ofstorage device or medium capable of storing data and/or othercomputer-readable instructions.

In certain embodiments, and with reference to exemplary computing system510 of FIG. 5, a communication interface, such as communicationinterface 522 in FIG. 5, may be used to provide connectivity betweeneach client system 610, 620, and 630 and network 650. Client systems610, 620, and 630 may be able to access information on server 640 or 645using, for example, a web browser or other client software. Suchsoftware may allow client systems 610, 620, and 630 to access datahosted by server 640, server 645, storage devices 660(1)-(N), storagedevices 670(1)-(N), storage devices 690(1)-(N), or intelligent storagearray 695. Although FIG. 6 depicts the use of a network (such as theInternet) for exchanging data, the embodiments described and/orillustrated herein are not limited to the Internet or any particularnetwork-based environment.

In at least one embodiment, all or a portion of one or more of theexemplary embodiments disclosed herein may be encoded as a computerprogram and loaded onto and executed by server 640, server 645, storagedevices 660(1)-(N), storage devices 670(1)-(N), storage devices690(1)-(N), intelligent storage array 695, or any combination thereof.All or a portion of one or more of the exemplary embodiments disclosedherein may also be encoded as a computer program, stored in server 640,run by server 645, and distributed to client systems 610, 620, and 630over network 650.

As detailed above, computing system 510 and/or one or more components ofnetwork architecture 600 may perform and/or be a means for performing,either alone or in combination with other elements, one or more steps ofan exemplary method for predicting security threat attacks.

While the foregoing disclosure sets forth various embodiments usingspecific block diagrams, flowcharts, and examples, each block diagramcomponent, flowchart step, operation, and/or component described and/orillustrated herein may be implemented, individually and/or collectively,using a wide range of hardware, software, or firmware (or anycombination thereof) configurations. In addition, any disclosure ofcomponents contained within other components should be consideredexemplary in nature since many other architectures can be implemented toachieve the same functionality.

In some examples, all or a portion of exemplary system 100 in FIG. 1 mayrepresent portions of a cloud-computing or network-based environment.Cloud-computing environments may provide various services andapplications via the Internet. These cloud-based services (e.g.,software as a service, platform as a service, infrastructure as aservice, etc.) may be accessible through a web browser or other remoteinterface. Various functions described herein may be provided through aremote desktop environment or any other cloud-based computingenvironment.

In various embodiments, all or a portion of exemplary system 100 in FIG.1 may facilitate multi-tenancy within a cloud-based computingenvironment. In other words, the software modules described herein mayconfigure a computing system (e.g., a server) to facilitatemulti-tenancy for one or more of the functions described herein. Forexample, one or more of the software modules described herein mayprogram a server to enable two or more clients (e.g., customers) toshare an application that is running on the server. A server programmedin this manner may share an application, operating system, processingsystem, and/or storage system among multiple customers (i.e., tenants).One or more of the modules described herein may also partition dataand/or configuration information of a multi-tenant application for eachcustomer such that one customer cannot access data and/or configurationinformation of another customer.

According to various embodiments, all or a portion of exemplary system100 in FIG. 1 may be implemented within a virtual environment. Forexample, the modules and/or data described herein may reside and/orexecute within a virtual machine. As used herein, the phrase “virtualmachine” generally refers to any operating system environment that isabstracted from computing hardware by a virtual machine manager (e.g., ahypervisor). Additionally or alternatively, the modules and/or datadescribed herein may reside and/or execute within a virtualizationlayer. As used herein, the phrase “virtualization layer” generallyrefers to any data layer and/or application layer that overlays and/oris abstracted from an operating system environment. A virtualizationlayer may be managed by a software virtualization solution (e.g., a filesystem filter) that presents the virtualization layer as though it werepart of an underlying base operating system. For example, a softwarevirtualization solution may redirect calls that are initially directedto locations within a base file system and/or registry to locationswithin a virtualization layer.

In some examples, all or a portion of exemplary system 100 in FIG. 1 mayrepresent portions of a mobile computing environment. Mobile computingenvironments may be implemented by a wide range of mobile computingdevices, including mobile phones, tablet computers, e-book readers,personal digital assistants, wearable computing devices (e.g., computingdevices with a head-mounted display, smartwatches, etc.), and the like.In some examples, mobile computing environments may have one or moredistinct features, including, for example, reliance on battery power,presenting only one foreground application at any given time, remotemanagement features, touchscreen features, location and movement data(e.g., provided by Global Positioning Systems, gyroscopes,accelerometers, etc.), restricted platforms that restrict modificationsto system-level configurations and/or that limit the ability ofthird-party software to inspect the behavior of other applications,controls to restrict the installation of applications (e.g., to onlyoriginate from approved application stores), etc. Various functionsdescribed herein may be provided for a mobile computing environmentand/or may interact with a mobile computing environment.

In addition, all or a portion of exemplary system 100 in FIG. 1 mayrepresent portions of, interact with, consume data produced by, and/orproduce data consumed by one or more systems for information management.As used herein, the phrase “information management” may refer to theprotection, organization, and/or storage of data. Examples of systemsfor information management may include, without limitation, storagesystems, backup systems, archival systems, replication systems, highavailability systems, data search systems, virtualization systems, andthe like.

In some embodiments, all or a portion of exemplary system 100 in FIG. 1may represent portions of, produce data protected by, and/or communicatewith one or more systems for information security. As used herein, thephrase “information security” may refer to the control of access toprotected data. Examples of systems for information security mayinclude, without limitation, systems providing managed securityservices, data loss prevention systems, identity authentication systems,access control systems, encryption systems, policy compliance systems,intrusion detection and prevention systems, electronic discoverysystems, and the like.

According to some examples, all or a portion of exemplary system 100 inFIG. 1 may represent portions of, communicate with, and/or receiveprotection from one or more systems for endpoint security. As usedherein, the phrase “endpoint security” may refer to the protection ofendpoint systems from unauthorized and/or illegitimate use, access,and/or control. Examples of systems for endpoint protection may include,without limitation, anti-malware systems, user authentication systems,encryption systems, privacy systems, spam-filtering services, and thelike.

The process parameters and sequence of steps described and/orillustrated herein are given by way of example only and can be varied asdesired. For example, while the steps illustrated and/or describedherein may be shown or discussed in a particular order, these steps donot necessarily need to be performed in the order illustrated ordiscussed. The various exemplary methods described and/or illustratedherein may also omit one or more of the steps described or illustratedherein or include additional steps in addition to those disclosed.

While various embodiments have been described and/or illustrated hereinin the context of fully functional computing systems, one or more ofthese exemplary embodiments may be distributed as a program product in avariety of forms, regardless of the particular type of computer-readablemedia used to actually carry out the distribution. The embodimentsdisclosed herein may also be implemented using software modules thatperform certain tasks. These software modules may include script, batch,or other executable files that may be stored on a computer-readablestorage medium or in a computing system. In some embodiments, thesesoftware modules may configure a computing system to perform one or moreof the exemplary embodiments disclosed herein.

In addition, one or more of the modules described herein may transformdata, physical devices, and/or representations of physical devices fromone form to another. Additionally or alternatively, one or more of themodules recited herein may transform a processor, volatile memory,non-volatile memory, and/or any other portion of a physical computingdevice from one form to another by executing on the computing device,storing data on the computing device, and/or otherwise interacting withthe computing device.

The preceding description has been provided to enable others skilled inthe art to best utilize various aspects of the exemplary embodimentsdisclosed herein. This exemplary description is not intended to beexhaustive or to be limited to any precise form disclosed. Manymodifications and variations are possible without departing from thespirit and scope of the instant disclosure. The embodiments disclosedherein should be considered in all respects illustrative and notrestrictive. Reference should be made to the appended claims and theirequivalents in determining the scope of the instant disclosure.

Unless otherwise noted, the terms “connected to” and “coupled to” (andtheir derivatives), as used in the specification and claims, are to beconstrued as permitting both direct and indirect (i.e., via otherelements or components) connection. In addition, the terms “a” or “an,”as used in the specification and claims, are to be construed as meaning“at least one of.” Finally, for ease of use, the terms “including” and“having” (and their derivatives), as used in the specification andclaims, are interchangeable with and have the same meaning as the word“comprising.”

What is claimed is:
 1. A computer-implemented method for predictingsecurity threat attacks, at least a portion of the method beingperformed by a computing device comprising at least one processor, themethod comprising: identifying candidate security threat targets withlatent attributes that describe features of the candidate securitythreat targets; identifying historical attack data that describes whichof the candidate security threat targets experienced an actual securitythreat attack; determining, by a software security prediction program, asimilarity relationship between latent attributes of at least onespecific candidate security threat target and latent attributes of thecandidate security threat targets that experienced the actual securitythreat attack according to the historical attack data by analyzing amatrix that indicates that the actual security threat attack targetedthe candidate security threat targets by populating a respective entryof the matrix at each intersection between a vector of the matrix thatcorresponds to the actual security threat attack and each vector of thematrix that corresponds to the candidate security threat targets thatexperienced the actual security threat attack; predicting by thesoftware security prediction program based on the determined similarityrelationship, that the specific candidate security threat target willexperience a future security threat attack; and performing, by thesoftware security prediction program, at least one remedial action toprotect the specific candidate security threat target in response topredicting the future security threat attack, wherein the candidatesecurity threat targets comprise enterprise organizations that includecustomers of a vendor of the software security prediction program. 2.The method of claim 1, wherein the actual security threat attackcomprises a malware attack.
 3. The method of claim 1, whereindetermining the similarity relationship comprises: identifying anadditional candidate security threat target that experienced a pair ofactual security threat attacks; and determining that the specificcandidate security threat target experienced one of the pair of actualsecurity threat attacks.
 4. The method of claim 3, wherein predictingthat the specific candidate security threat target will experience thefuture security threat attack comprises predicting that the specificcandidate security threat target will experience the other of the pairof actual security threat attacks.
 5. The method of claim 1, whereindetermining the similarity relationship comprises: identifying anadditional candidate security threat target that stored a cluster ofbenign files and that experienced a same security threat attack as thepredicted future security threat attack; and determining that thespecific candidate security threat target also stored the cluster ofbenign files.
 6. The method of claim 1, wherein the matrix identifies:the enterprise organizations as corresponding to one of rows and columnsof the matrix; and security threat attacks corresponding to the other ofthe rows and columns of the matrix.
 7. The method of claim 6, whereinthe matrix comprises a sparse matrix.
 8. The method of claim 6, whereindetermining the similarity relationship comprises performing a rankfactorization of the matrix.
 9. The method of claim 8, whereinperforming the rank factorization of the matrix comprises executing astochastic gradient descent algorithm.
 10. The method of claim 1,wherein: determining the similarity relationship comprises: rankingcandidate security threat targets in terms of counts of experiencingactual security threat attacks; and ranking security threat attacks interms of actually attacking enterprise organizations; and predictingthat the specific candidate security threat target will experience thefuture security threat attack is based on the rank of the specificcandidate security threat target and the rank of the predicted futuresecurity threat attack.
 11. A system for predicting security threatattacks, the system comprising: an identification module, stored inmemory, that: identifies candidate security threat targets with latentattributes that describe features of the candidate security threattargets; and identifies historical attack data that describes which ofthe candidate security threat targets experienced an actual securitythreat attack; a determination module, stored in memory, thatdetermines, as part of a software security prediction program,similarity relationship between latent attributes of at least onespecific candidate security threat target and latent attributes of thecandidate security threat targets that experienced the actual securitythreat attack according to the historical attack data by analyzing amatrix that indicates that the actual security threat attack targetedthe candidate security threat targets by populating a respective entryof the matrix at each intersection between a vector of the matrix thatcorresponds to the actual security threat attack and each vector of thematrix that corresponds to the candidate security threat targets thatexperienced the actual security threat attack; a prediction module,stored in memory, that predicts, as part of the software securityprediction program based on the determined similarity relationship, thatthe specific candidate security threat target will experience a futuresecurity threat attack; a performance module, stored in memory, thatperforms, as part of the software security prediction program, at leastone remedial action to protect the specific candidate security threattarget in response to predicting the future security threat attack,wherein the candidate security threat targets comprise enterpriseorganizations that include customers of a vendor of the softwaresecurity prediction program; and at least one physical processorconfigured to execute the identification module, the determinationmodule, the prediction module, and the performance module.
 12. Thesystem of claim 11, wherein the actual security threat attack comprisesa malware attack.
 13. The system of claim 12, wherein the determinationmodule determines the similarity relationship by: identifying anadditional candidate security threat target that experienced a pair ofactual security threat attacks; and determining that the specificcandidate security threat target experienced one of the pair of actualsecurity threat attacks.
 14. The system of claim 13, wherein theprediction module predicts that the specific candidate security threattarget will experience the future security threat attack by predictingthat the specific candidate security threat target will experience theother of the pair of actual security threat attacks.
 15. The system ofclaim 11, wherein the determination module determines the similarityrelationship by: identifying an additional candidate security threattarget that stored a cluster of benign files and that experienced a samesecurity threat attack as the predicted future security threat attack;and determining that the specific candidate security threat target alsostored the cluster of benign files.
 16. The system of claim 11, whereinthe matrix identifies: the enterprise organizations as corresponding toone of rows and columns of the matrix; and security threat attackscorresponding to the other of the rows and columns of the matrix. 17.The system of claim 16, wherein the matrix comprises a sparse matrix.18. The system of claim 16, wherein the determination module determinesthe similarity relationship by performing a rank factorization of thematrix.
 19. The system of claim 18, wherein the determination moduleperforms the rank factorization of the matrix by executing a stochasticgradient descent algorithm.
 20. A non-transitory computer-readablemedium comprising one or more computer-readable instructions that, whenexecuted by at least one processor of a computing device, cause thecomputing device to: identify candidate security threat targets withlatent attributes that describe features of the candidate securitythreat targets; identify historical attack data that describes which ofthe candidate security threat targets experienced an actual securitythreat attack; determine, by a software security prediction program, asimilarity relationship between latent attributes of at least onespecific candidate security threat target and latent attributes of thecandidate security threat targets that experienced the actual securitythreat attack according to the historical attack data by analyzing amatrix that indicates that the actual security threat attack targetedthe candidate security threat targets by populating a respective entryof the matrix at each intersection between a vector of the matrix thatcorresponds to the actual security threat attack and each vector of thematrix that corresponds to the candidate security threat targets thatexperienced the actual security threat attack; predict, by the softwaresecurity prediction program based on the determined similarityrelationship, that the specific candidate security threat target willexperience a future security threat attack; and perform, by the softwaresecurity prediction program, at least one remedial action to protect thespecific candidate security threat target in response to predicting thefuture security threat attack, wherein the candidate security threattargets comprise enterprise organizations that include customers of avendor of the software security prediction program.